jasiakj


Joann Jasiak

Photo of Joann Jasiak

Department of Economics

Professor

Office: Vari Hall, 1062
Phone: (416)736-2100 Ext: 77045
Email: jasiakj@yorku.ca
Primary website: http://www.jjstats.com


I am a Professor in the Department of Economics at York University. My research interests are in econometrics and time series analysis. Recently, I have been working on estimation and inference methods for noncausal processes and stationary martingales, and their applications to financial and economic data. I obtained my PhD from the University of Montreal.

More...
Other

Publication
Year

Granularity Adjustment for Default Risk Factor Model with Cohorts
(with C. Gourieroux)
Journal of Banking and Finance , 6(5), 2012, 1464 -1477
Abstract: This paper examines granularity adjustments to parameter estimators in a default risk model with cohorts. The model is an extension of the Vasicek model (Vasicek, 1991) and includes a general factor and cohort specific factors. The granularity adjustments derived in the paper concern the mean and/or the variance of observed default frequencies and are easy to implement in practice. For illustration, the method is applied to the S&P corporate ratings. The Granularity Adjusted (GA) estimators are compared to the unadjusted estimators in terms of their asymptotic properties and in finite sample.
[go to paper]

L-Performance with an Application to Hedge Funds
(with Serge Darolles and Christian Gourieroux)
Journal of Empirical Finance , 16(4), 2009, 671-685
Abstract: This paper introduces a new parametric fund performance measure, called the L-performance. The L-performance is an alternative to the Sharpe performance, which is commonly used in practice despite its inability to account for skewness and heavy tails of unconditional return distributions. The L-performance improves upon the Sharpe measure in this respect. Technically, it resembles the Sharpe measure in that it is defined as a ratio of the first- and second-order moments, which are the trimmed L-moments instead of the conventional (power) moments. The trimming parameters allow for focusing the L-performance on specific risk levels of interest, according to financial risk criteria. For illustration, a set of L-performances is computed for a variety of hedge funds. The empirical study shows the use of L-performance for fund ranking and return smoothing (manipulation) control.
[go to paper]

The Wishart Autoregressive Process of Multivariate Stochastic Volatility
(with Christian Gourieroux and Razvan Sufana)
Journal of Econometrics , 150(2), 2009, 167-181
Abstract: The Wishart Autoregressive (WAR) process is a dynamic model for time series of multivariate stochastic volatility. The WAR naturally accommodates the positivity and symmetry of volatility matrices and provides closed-form non-linear forecasts. The estimation of the WAR is straighforward, as it relies on standard methods such as the Method of Moments and Maximum Likelihood. For illustration, the WAR is applied to a sequence of intraday realized volatility-covolatility matrices from the Toronto Stock Market (TSX).
[go to paper]

Dynamic Quantile Models
(with Christian Gourieroux)
Journal of Econometrics , 147(1), 2009, 198-205
Abstract: This paper introduces the Dynamic Additive Quantile (DAQ) model that ensures the monotonicity of conditional quantile estimates. The DAQ model is easily estimable and can be used for computation and updating of the Value-at-Risk. An asymptotically efficient estimator of the DAQ is obtained by maximizing an objective function based on the inverse KLIC measure. An alternative estimator proposed in the paper is the Method of L-Moments estimator (MLM). The MLM estimator is consistent, but generally not fully efficient. Goodness-of-fit tests and diagnostic tools for the assessment of the model are also provided. For illustration, the DAQ model is estimated from a series of returns on the Toronto Stock Exchange (TSX) market index.
[go to paper]

The Ordered Qualitative Model for Credit Rating Transitions
(with Dingan Feng and Christian Gourieroux)
Journal of Empirical Finance , 15(1), 2008, 111-130
Abstract: Information on the expected changes in credit quality of obligors is contained in credit migration matrices which trace out the movements of firms across ratings categories in a given period of time and in a given group of bond issuers. The rating matrices provided by Moody's, Standard &Poor's and Fitch became crucial inputs to many applications, including the assessment of risk on corporate credit portfolios (CreditVar) and credit derivatives pricing. We propose a factor probit model for modeling and prediction of credit rating matrices that are assumed to be stochastic and driven by a latent factor. The filtered latent factor path reveals the effect of the economic cycle on corporate credit ratings, and provides evidence in support of the PIT (point-in-time) rating philosophy. The factor probit model also yields the estimates of cross-sectional correlations in rating transitions that are documented empirically but not fully accounted for in the literature and in the regulatory rules established by the Basle Committee.
[go to paper]

Structural Laplace Transform and Compound Autoregressive Models
(with S. Darolles and Christian Gourieroux)
Journal of Time Series Analysis , 27(4), 2006, 477-50
Abstract: This paper presents a new general class of compound autoregressive (Car) models for non-Gaussian time series. The distinctive feature of the class is that Car models are specified by means of the conditional Laplace transforms. This approach allows for simple derivation of the ergodicity conditions and ensures the existence of forecasting distributions in closed form, at any horizon. The last property is of particular interest for applications to finance and economics that investigate the term structure of variables and/or of their nonlinear transforms. The Car class includes a number of time-series models that already exist in the literature, as well as new models introduced in this paper. Their applications are illustrated by examples of portfolio management, term structure and extreme risk analysis.
[go to paper]




Upcoming Courses

Term Course Number Section Title Type
Fall 2018 AP/ECON4140 3.0 B Financial Econometrics LECT
Winter 2019 AP/ECON4140 3.0 N Financial Econometrics LECT
Fall 2018 GS/ECON6220 3.0 A Advanced Econometric Theory I LECT
Fall 2018 GS/ECON5025 3.0 A Applied Econometrics LECT


I am a Professor in the Department of Economics at York University. My research interests are in econometrics and time series analysis. Recently, I have been working on estimation and inference methods for noncausal processes and stationary martingales, and their applications to financial and economic data. I obtained my PhD from the University of Montreal.

All Publications


Other

Publication
Year

Granularity Adjustment for Default Risk Factor Model with Cohorts
(with C. Gourieroux)
Journal of Banking and Finance , 6(5), 2012, 1464 -1477
Abstract: This paper examines granularity adjustments to parameter estimators in a default risk model with cohorts. The model is an extension of the Vasicek model (Vasicek, 1991) and includes a general factor and cohort specific factors. The granularity adjustments derived in the paper concern the mean and/or the variance of observed default frequencies and are easy to implement in practice. For illustration, the method is applied to the S&P corporate ratings. The Granularity Adjusted (GA) estimators are compared to the unadjusted estimators in terms of their asymptotic properties and in finite sample.
[go to paper]

L-Performance with an Application to Hedge Funds
(with Serge Darolles and Christian Gourieroux)
Journal of Empirical Finance , 16(4), 2009, 671-685
Abstract: This paper introduces a new parametric fund performance measure, called the L-performance. The L-performance is an alternative to the Sharpe performance, which is commonly used in practice despite its inability to account for skewness and heavy tails of unconditional return distributions. The L-performance improves upon the Sharpe measure in this respect. Technically, it resembles the Sharpe measure in that it is defined as a ratio of the first- and second-order moments, which are the trimmed L-moments instead of the conventional (power) moments. The trimming parameters allow for focusing the L-performance on specific risk levels of interest, according to financial risk criteria. For illustration, a set of L-performances is computed for a variety of hedge funds. The empirical study shows the use of L-performance for fund ranking and return smoothing (manipulation) control.
[go to paper]

The Wishart Autoregressive Process of Multivariate Stochastic Volatility
(with Christian Gourieroux and Razvan Sufana)
Journal of Econometrics , 150(2), 2009, 167-181
Abstract: The Wishart Autoregressive (WAR) process is a dynamic model for time series of multivariate stochastic volatility. The WAR naturally accommodates the positivity and symmetry of volatility matrices and provides closed-form non-linear forecasts. The estimation of the WAR is straighforward, as it relies on standard methods such as the Method of Moments and Maximum Likelihood. For illustration, the WAR is applied to a sequence of intraday realized volatility-covolatility matrices from the Toronto Stock Market (TSX).
[go to paper]

Dynamic Quantile Models
(with Christian Gourieroux)
Journal of Econometrics , 147(1), 2009, 198-205
Abstract: This paper introduces the Dynamic Additive Quantile (DAQ) model that ensures the monotonicity of conditional quantile estimates. The DAQ model is easily estimable and can be used for computation and updating of the Value-at-Risk. An asymptotically efficient estimator of the DAQ is obtained by maximizing an objective function based on the inverse KLIC measure. An alternative estimator proposed in the paper is the Method of L-Moments estimator (MLM). The MLM estimator is consistent, but generally not fully efficient. Goodness-of-fit tests and diagnostic tools for the assessment of the model are also provided. For illustration, the DAQ model is estimated from a series of returns on the Toronto Stock Exchange (TSX) market index.
[go to paper]

The Ordered Qualitative Model for Credit Rating Transitions
(with Dingan Feng and Christian Gourieroux)
Journal of Empirical Finance , 15(1), 2008, 111-130
Abstract: Information on the expected changes in credit quality of obligors is contained in credit migration matrices which trace out the movements of firms across ratings categories in a given period of time and in a given group of bond issuers. The rating matrices provided by Moody's, Standard &Poor's and Fitch became crucial inputs to many applications, including the assessment of risk on corporate credit portfolios (CreditVar) and credit derivatives pricing. We propose a factor probit model for modeling and prediction of credit rating matrices that are assumed to be stochastic and driven by a latent factor. The filtered latent factor path reveals the effect of the economic cycle on corporate credit ratings, and provides evidence in support of the PIT (point-in-time) rating philosophy. The factor probit model also yields the estimates of cross-sectional correlations in rating transitions that are documented empirically but not fully accounted for in the literature and in the regulatory rules established by the Basle Committee.
[go to paper]

Structural Laplace Transform and Compound Autoregressive Models
(with S. Darolles and Christian Gourieroux)
Journal of Time Series Analysis , 27(4), 2006, 477-50
Abstract: This paper presents a new general class of compound autoregressive (Car) models for non-Gaussian time series. The distinctive feature of the class is that Car models are specified by means of the conditional Laplace transforms. This approach allows for simple derivation of the ergodicity conditions and ensures the existence of forecasting distributions in closed form, at any horizon. The last property is of particular interest for applications to finance and economics that investigate the term structure of variables and/or of their nonlinear transforms. The Car class includes a number of time-series models that already exist in the literature, as well as new models introduced in this paper. Their applications are illustrated by examples of portfolio management, term structure and extreme risk analysis.
[go to paper]




Upcoming Courses

Term Course Number Section Title Type
Fall 2018 AP/ECON4140 3.0 B Financial Econometrics LECT
Winter 2019 AP/ECON4140 3.0 N Financial Econometrics LECT
Fall 2018 GS/ECON6220 3.0 A Advanced Econometric Theory I LECT
Fall 2018 GS/ECON5025 3.0 A Applied Econometrics LECT